What are the common loss functions used in deep learning? How do they impact the training process?

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Krishnapriya Rajeev · Answer

In deep learning, a loss function measures the discrepancy between the predicted output and the true output of a neural network. The goal of training a neural network is to minimize the value of the loss function.

A few loss functions used in deep learning are:

Mean Squared Error (MSE): This loss function is commonly used for regression problems, where the goal is to predict a continuous variable. MSE measures the average squared difference between the predicted and actual values. The higher the difference, the higher the loss value. Gradient descent methods can be used to optimize MSE because it is differentiable and convex.
Binary Cross-Entropy: This loss function is used for binary classification problems, where the output is either 0 or 1. Binary cross-entropy measures the distance between the predicted and actual binary values. It is commonly used in logistic regression and sigmoid activation functions.
Categorical Cross-Entropy: This loss function is used for multi-class classification problems, where the output can take more than two values. It is commonly used in softmax activation functions. The categorical cross-entropy loss function measures the distance between the predicted probability distribution and the true probability distribution.
KL Divergence: KL divergence is a measure of the difference between two probability distributions. Generative models like Restricted Boltzmann Machines and Variational Autoencoders tend to use KL divergence.

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What are the common loss functions used in deep learning? How do they impact the training process?

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